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Prediction of effluent quality in ICEAS-sequential batch reactor using feedforward artificial neural network

WATER SCIENCE AND TECHNOLOGY 2019年第2期80卷 213-222页

作者： Khatri, Narendra Khatri, Kamal Kishore Sharma, Abhishek LNM Inst Informat Technol Dept Mech Mechatron Engn Jaipur 302031 Rajasthan India LNM Inst Informat Technol Dept Elect & Commun Engn Jaipur 302031 Rajasthan India

It is highly essential that municipal wastewater is treated before its discharge and reuse in order to meet the standard requirements for safe marine life and for farming and industries. It is beneficial to use reclaimed water, since availability of fresh water is inadequate. An investigation was conducted on the Jamnagar Municipal Corporation Sewage Treatment Plant (JMC-STP) to develop a feedforward artificial neural network (FF-ANN) model. It is an alternate for the modelling/prediction of JMC-STP to circumvent over the versatile physical, chemical, and biological treatment process simulations. The models were developed to predict effluent quality parameters through influent characteristics. The parameters are pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), total Kjeldahl nitrogen (TKN), ammonium nitrogen (AN) and total phosphorus (TP). The correlation coefficient R-TRAINING and R-ALL were calculated for all parametric models. The MAD (mean absolute deviation), MSE (mean square error), RMSE (root mean square error) and MAPE (mean absolute percentage error) were evaluated for FF-ANN models. This proves to be a useful tool for the plant management to optimize the treatment quality as it enhances the performance and reliability of the plant. The simulation results were validated through the measured values.

关键词： feedforward artificial neural network ICEAS-sequential batch reactor prediction of effluent quality sewage treatment plant

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A formal selection and pruning algorithm for feedforward artificial neural network optimization

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IEEE TRANSACTIONS ON neural networkS 1999年第4期10卷 964-968页

作者： Ponnapalli, PVS Ho, KC Thomson, M Manchester Metropolitan Univ Dept Elect & Elect Engn Manchester M1 5GD Lancs England

A formal selection and pruning technique based on the concept of local relative sensitivity index is proposed for feedforward artificial neural networks. The mechanism of backpropagation training algorithm: is revisited and the theoretical foundation of the improved selection and pruning technique is presented. This technique Is based on parallel pruning of weights which are relatively redundant in a subgroup of a feedforward neural network. Comparative studies with a similar technique proposed in the literature show that the improved technique provides better pruning results in terms of reduction of model residues, improvement, of generalization capability and reduction of network complexity;The effectiveness of the improved technique is demonstrated in developing neural network (NN) models of a number of nonlinear systems including three bit parity problem, Van der Pol equation, a chemical processes and two: nonlinear discrete-time systems using the backpropagation training algorithm with adaptive learning late.

关键词： backpropagation feedforward artificial neural network optimization pruning

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Prediction of equilibrium water dew point of natural gas in TEG dehydration systems using Bayesian feedforward artificial neural network (FANN)

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PETROLEUM SCIENCE AND TECHNOLOGY 2018年第20期36卷 1620-1626页

作者： Ahmad, Z. Bahadori, Alireza Zhang, Jie Univ Sains Malaysia Sch Chem Engn Engn Campus Nibong Tebal 14300 Penang Malaysia Southern Cross Univ Sch Environm Sci & Engn Lismore NSW Australia Newcastle Univ Sch Chem Engn & Adv Mat Newcastle Upon Tyne Tyne & Wear England

The aim of this paper is to predict the equilibrium water dew point of natural gas in TEG dehydration process using feedforward artificial neural network (FANN). The ANN model shows a good result as the coefficient of determination of 0.9989 and 0.9976 was obtained for training and testing data respectively with relatively small value of mean square errors of 0.0203 and 0.0221. 0.5% of average absolute deviation percentage was observed which is comparable with the literatures. It clearly shows that FANN gives a good prediction on water dew point of natural gas in TEG dehydration process.

关键词： artificial intelligent equilibrium water dew point feedforward artificial neural network process modeling TEG dehydration process

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Evaluating the Influence of Morphological Features on the Vulnerability of Lipid-Rich Plaques During Stenting

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JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME 2025年第2期147卷 021007页

作者： Colmenarez, Jose A. Dong, Pengfei Lee, Juhwan Wilson, David L. Gu, Linxia Florida Inst Technol Dept Biomed & Chem Engn & Sci 150 West Univ Blvd Melbourne FL 32901 USA Case Western Reserve Univ Dept Biomed Engn 10900 Euclid Ave Cleveland OH 44106 USA

Lipid-rich atheromas are linked to plaque rupture in stented atherosclerotic arteries. While fibrous cap thickness is acknowledged as a critical indicator of vulnerability, it is likely that other morphological features also exert influence. However, detailed quantifications of their contributions and intertwined effects in stenting are lacking. Therefore, our goal is to assess the impact of plaque characteristics on the fibrous cap stress and elucidate their underlying mechanisms. We analyzed the stent deployment in a three-dimensional patient-specific coronary artery reconstructed from intravascular optical coherence tomography (IVOCT) data using the finite element method. Additionally, we performed sensitivity analysis on 78,000 distinct plaque geometries of two-dimensional arterial cross section for verification. Results from the three-dimensional patient-specific model indicate strong correlations between maximum fibrous cap stress and lipid arc (r = 0.769), area stenosis (r = 0.550), and lumen curvature (r = 0.642). Plaques with lipid arcs > 60 deg, area stenosis > 75 % , and lumen curvatures > 5 mm(-1) are at rupture risk. While we observed a rise in stress with thicker lipid cores, it was less representative than other features. Fibrous cap thickness showed a poor correlation, with the sensitivity analysis revealing its significance only when high stretches are induced by other features, likely due to its J-shaped stress-stretch response. Contrary to physiological pressure, the stent expansion generates unique vulnerable features as the stent load-transferring characteristics modify the plaque's response. This study is expected to prompt further clinical investigations of other morphological features forpredictingplaque rupture in stenting.

关键词： plaque vulnerability thin-cap fibroatheroma atherosclerosis percutaneous coronary intervention optical coherence tomography finite element analysis linear regression variance-based sensitivity analysis Monte Carlo-based procedure feedforward artificial neural network

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artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions

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IET RENEWABLE POWER GENERATION 2009年第2期3卷 239-253页

作者： Syafaruddin Karatepe, E. Hiyama, T. Kumamoto Univ Dept Comp Sci & Elect Engn Kumamoto 8608555 Japan Ege Univ Dept Elect & Elect Engn TR-35100 Bornova Turkey

The one of main causes of reducing energy yield of photovoltaic systems is partially shaded conditions. Although the conventional maximum power point tracking (MPPT) control algorithms operate well under uniform insolation, they do not operate well in non-uniform insolation. The non-uniform conditions cause multiple local maximum power points on the power-voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global maximum power point (MPP) may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognise the global operating point under partially shaded conditions. In this paper, a novel MPPT system is proposed for partially shaded PV array using artificial neural network (ANN) and fuzzy logic with polar information controller. The ANN with three layer feed-forward is trained once for several partially shaded conditions to determine the global MPP voltage. The fuzzy logic with polar information controller uses the global MPP voltage as a reference voltage to generate the required control signal for the power converter. Another objective of this study is to determine the estimated maximum power and energy generation of PV system through the same ANN structure. The effectiveness of the proposed method is demonstrated under the experimental real-time simulation technique based dSPACE real-time interface system for different interconnected PV arrays such as series-parallel, bridge link and total cross tied configurations.

关键词： maximum power point tracking control algorithms polar information controller partially shaded photovoltaic array dSPACE photovoltaic systems Control of electric power systems power engineering computing feedforward neural nets neural computing techniques Power system control Power engineering computing polar coordinated fuzzy controller feedforward artificial neural network fuzzy control Solar power stations and photovoltaic power systems power system control photovoltaic power systems Fuzzy control fuzzy logic

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Modeling and Optimizing Tensile Strength and Yield Point on a Steel Bar Using an artificial neural network With Taguchi Particle Swarm Optimizer

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IEEE ACCESS 2016年 4卷 585-593页

作者： Chou, Ping-Yi Tsai, Jinn-Tsong Chou, Jyh-Horng Natl Kaohsiung First Univ Sci & Technol Inst Engn Sci & Technol Kaohsiung 824 Taiwan Natl Pingtung Univ Dept Comp Sci Pingtung 900 Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Natl Kaohsiung First Univ Sci & Technol Inst Elect Engn Kaohsiung 824 Taiwan Kaohsiung Med Univ Dept Healthcare Adm & Med Informat Kaohsiung 807 Taiwan

A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural network (ANN) is used to model and optimize the chemical composition of a steel bar. The novel contribution of a TPSO is the use of a Taguchi method mechanism to exploit better solutions in the search space through iterations, the use of the conventional non-linear PSO to increase convergence speed, and the use of random movement for particle diversity. The exploration and exploitation capability of the TPSO were confirmed by performance comparisons with other PSO-based algorithms in solving high-dimensional global numerical optimization problems. Experiments in this paper showed that the TPSO provides higher computational efficiency and higher robustness when solving problems involving seven non-linear benchmark functions, including three unimodal functions, one multimodal functions, two rotated functions, and one shifted functions. The results for the computational experiments show that the TPSO outperforms other PSO-based algorithms reported in the literature. Finally, the results obtained by a TPSO-based ANN model of the chemical composition of the steel bar were consistent with the actual data. That is, the proposed TPSO with three-layer feedforward ANN can be used in practical applications.

关键词： Taguchi method particle swarm optimization feedforward artificial neural network chemical composition of steel bar yield point tensile strength

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On some peculiarities of neural network approximation applied to the inverse kinematics problem

On some peculiarities of neural network approximation applie...

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Conference on Control and Fault-Tolerant Systems (SysTol)

作者： Bartecki, Krzysztof Opole Univ Technol Inst Control & Comp Engn Ul Sosnkowskiego 31 PL-45272 Opole Poland

ISBN: (纸本)9781424481545

Characteristic features of feedforward artificial neural networks, acting as universal function approximators, are presented. The problem under consideration concerns inverse kinematics of a two-link planar manipulator. As shown in the article, a two-layer, feedforward neural network is able to learn the nonlinear mapping between the end-effector position domain and the joint angle domain of the manipulator. However, the necessary condition for achieving the required approximation quality is the selection of suitable network structure, especially with regard to the number of nonlinear, sigmoidal units in its hidden layer. Effects of learning algorithm and choice of learning data set on the network performance are also demonstrated.

关键词： dexterous manipulators feedforward artificial neural network inverse kinematic problem inverse problems learning (artificial intelligence) learning algorithm neural network approximation nonlinear mapping two link planar manipulator

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Increasing the effectiveness of handwritten Manipuri Meetei-Mayek character recognition using multiple-HOG-feature descriptors

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2019年第5期44卷 1-14页

作者： Nongmeikapam, Kishorjit Kumar, Wahengbam Kanan Meetei, Oinam Nickson Tuithung, Themrichon Indian Inst Informat Technol Manipur Dept Comp Sci & Engn Imphal Manipur India North Eastern Reg Inst Sci & Technol Dept Elect & Commun Engn Nirjuli India Natl Inst Technol Dept Comp Sci & Engn Nagaland Dimapur India

Detection and reading of the text from natural images is a difficult computer vision task, which is essential in a variety of emerging applications. Document character recognition is one such problem, which has been widely studied and documented by many machine learning and computer vision researchers, which is practically used for solving applications like recognizing handwritten digits. In this paper, a new approach for efficiently extracting cognition out of a total of 56 different classes of Handwritten Manipuri Meetei-Mayek (HMMM) (an Indian language) is described. Although character recognition algorithms have been researched and developed for other Indian scripts, no research work has been reported so far for recognizing all the characters of the Manipuri Meetei-Mayek (MMM). The work begins with a thorough analysis of the recognition task using a single hidden layer type Multilayer Perceptron feedforward artificial neural network with Histogram of Oriented Gradient (HOG) feature descriptors. After reviewing the level of accuracy and time it takes to train the network, the limitations are experimentally removed using multiple-sized cell grids using HOG descriptors. HOG, being a gradient-based descriptor, is very efficient in data discrimination and very stable with illumination variation. For efficient classification of the HOG features of the MMM, a linear multiclass support vector machine (SVM) classifier has been proposed for classifying the different offline characters because of its simplicity and speed. The classification based on linear multiclass SVM yielded a very high overall accuracy of 96.928%

关键词： Manipuri Meetei-Mayek (script) multilayer perceptron feedforward artificial neural network histogram of oriented gradient (HOG) linear multiclass support vector machine (SVM)

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Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Systems

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IEEE ACCESS 2019年 7卷 91463-91475页

作者： Eseye, Abinet Tesfaye Lehtonen, Matti Tukia, Toni Uimonen, Semen Millar, R. John Aalto Univ Dept Elect Engn & Automat Espoo 02150 Finland

Improved performance electricity demand forecast can provide decentralized energy system operators, aggregators, managers, and other stakeholders with essential information for energy resource scheduling, demand response management, and energy market participation. Most previous methodologies have focused on predicting the aggregate amount of electricity demand at national or regional scale and disregarded the electricity demand for small-scale decentralized energy systems (buildings, energy communities, microgrids, local energy internets, etc.), which are emerging in the smart grid context. Furthermore, few research groups have performed attribute selection before training predictive models. This paper proposes a machine learning (ML)-based integrated feature selection approach to obtain the most relevant and nonredundant predictors for accurate short-term electricity demand forecasting in distributed energy systems. In the proposed approach, one of the ML tools - binary genetic algorithm (BGA) is applied for the feature selection process and Gaussian process regression (GPR) is used for measuring the fitness score of the features. In order to validate the effectiveness of the proposed approach, it is applied to various building energy systems located in the Otaniemi area of Espoo, Finland. The findings are compared with those achieved by other feature selection techniques. The proposed approach enhances the quality and efficiency of the predictor selection, with minimal chosen predictors to achieve improved prediction accuracy. It outperforms the other evaluated feature selection methods. Besides, a feedforward artificial neural network (FFANN) model is implemented to evaluate the forecast performance of the selected predictor subset. The model is trained using two-year hourly dataset and tested with another one-year hourly dataset. The obtained results verify that the FFANN forecast model based on the BGA-GPR FS selected training feature subset has achieved an an

关键词： Binary genetic algorithm decentralized energy system electricity demand forecasting feature selection feedforward artificial neural network fitness evaluation measure Gaussian process regression machine learning smart grid

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Optimization of a quantum-dot semiconductor optical amplifier (QD-SOA) design using the genetic algorithm

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OPTICAL AND QUANTUM ELECTRONICS 2020年第1期52卷 1-14页

作者： Hakimian, Farideh Shayesteh, Mohammad Reza Moslemi, Mohammad Reza Islamic Azad Univ Yazd Branch Dept Elect Engn Yazd Iran Islamic Azad Univ Zarghan Branch Dept Elect Engn Zarghan Iran

In this paper, we proposed an intelligence model for the optimal design of the quantum-dot semiconductor optical amplifier (QD-SOA). The intelligence model is designed using the artificial neural network (ANN) and genetic algorithm (GA). We first present a simple, accurate, and fast model based on the feedforward ANN. The required data for training the ANN are collected from the numerical solution. The rate equations based on the occupation probability of energy levels and wave propagation equations in the active region of the QD-SOA are solved numerically to calculate optical gain using the slice technique. Then, by using the obtained ANN model and the GA, a new method to design the optimal structure parameters of the QD-SOA is proposed.

关键词： Quantum dot-semiconductor optical amplifier (QD-SOA) Optimal design feedforward artificial neural network Genetic algorithm

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